Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations32951
Missing cells2448
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory72.0 B

Variable types

Text2
Numeric7

Alerts

product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_weight_g is highly overall correlated with product_height_cm and 2 other fieldsHigh correlation
product_width_cm is highly overall correlated with product_length_cm and 1 other fieldsHigh correlation
product_category_name has 610 (1.9%) missing valuesMissing
product_name_lenght has 610 (1.9%) missing valuesMissing
product_description_lenght has 610 (1.9%) missing valuesMissing
product_photos_qty has 610 (1.9%) missing valuesMissing
product_id has unique valuesUnique

Reproduction

Analysis started2024-08-24 08:08:44.674508
Analysis finished2024-08-24 08:08:52.523822
Duration7.85 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

product_id
Text

UNIQUE 

Distinct32951
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size257.6 KiB
2024-08-24T17:08:52.740074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters1054432
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32951 ?
Unique (%)100.0%

Sample

1st row1e9e8ef04dbcff4541ed26657ea517e5
2nd row3aa071139cb16b67ca9e5dea641aaa2f
3rd row96bd76ec8810374ed1b65e291975717f
4th rowcef67bcfe19066a932b7673e239eb23d
5th row9dc1a7de274444849c219cff195d0b71
ValueCountFrequency (%)
1e9e8ef04dbcff4541ed26657ea517e5 1
 
< 0.1%
ac5bae85a895724330c4acddd581b02b 1
 
< 0.1%
cef67bcfe19066a932b7673e239eb23d 1
 
< 0.1%
9dc1a7de274444849c219cff195d0b71 1
 
< 0.1%
41d3672d4792049fa1779bb35283ed13 1
 
< 0.1%
732bd381ad09e530fe0a5f457d81becb 1
 
< 0.1%
2548af3e6e77a690cf3eb6368e9ab61e 1
 
< 0.1%
37cc742be07708b53a98702e77a21a02 1
 
< 0.1%
8c92109888e8cdf9d66dc7e463025574 1
 
< 0.1%
14aa47b7fe5c25522b47b4b29c98dcb9 1
 
< 0.1%
Other values (32941) 32941
> 99.9%
2024-08-24T17:08:53.145942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 66459
 
6.3%
3 66385
 
6.3%
e 66223
 
6.3%
c 66221
 
6.3%
9 66068
 
6.3%
1 66065
 
6.3%
d 66015
 
6.3%
5 65942
 
6.3%
7 65894
 
6.2%
f 65852
 
6.2%
Other values (6) 393308
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 659325
62.5%
Lowercase Letter 395107
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 66459
10.1%
3 66385
10.1%
9 66068
10.0%
1 66065
10.0%
5 65942
10.0%
7 65894
10.0%
2 65820
10.0%
4 65817
10.0%
0 65562
9.9%
6 65313
9.9%
Lowercase Letter
ValueCountFrequency (%)
e 66223
16.8%
c 66221
16.8%
d 66015
16.7%
f 65852
16.7%
b 65644
16.6%
a 65152
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 659325
62.5%
Latin 395107
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
8 66459
10.1%
3 66385
10.1%
9 66068
10.0%
1 66065
10.0%
5 65942
10.0%
7 65894
10.0%
2 65820
10.0%
4 65817
10.0%
0 65562
9.9%
6 65313
9.9%
Latin
ValueCountFrequency (%)
e 66223
16.8%
c 66221
16.8%
d 66015
16.7%
f 65852
16.7%
b 65644
16.6%
a 65152
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1054432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 66459
 
6.3%
3 66385
 
6.3%
e 66223
 
6.3%
c 66221
 
6.3%
9 66068
 
6.3%
1 66065
 
6.3%
d 66015
 
6.3%
5 65942
 
6.3%
7 65894
 
6.2%
f 65852
 
6.2%
Other values (6) 393308
37.3%

product_category_name
Text

MISSING 

Distinct73
Distinct (%)0.2%
Missing610
Missing (%)1.9%
Memory size257.6 KiB
2024-08-24T17:08:53.416762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length46
Median length32
Mean length14.958659
Min length3

Characters and Unicode

Total characters483778
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowperfumaria
2nd rowartes
3rd rowesporte_lazer
4th rowbebes
5th rowutilidades_domesticas
ValueCountFrequency (%)
cama_mesa_banho 3029
 
9.4%
esporte_lazer 2867
 
8.9%
moveis_decoracao 2657
 
8.2%
beleza_saude 2444
 
7.6%
utilidades_domesticas 2335
 
7.2%
automotivo 1900
 
5.9%
informatica_acessorios 1639
 
5.1%
brinquedos 1411
 
4.4%
relogios_presentes 1329
 
4.1%
telefonia 1134
 
3.5%
Other values (63) 11596
35.9%
2024-08-24T17:08:53.799150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 57759
11.9%
e 57376
11.9%
o 49486
10.2%
s 48323
10.0%
i 31889
 
6.6%
_ 30870
 
6.4%
r 29702
 
6.1%
t 24536
 
5.1%
c 23062
 
4.8%
m 21106
 
4.4%
Other values (18) 109669
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 452813
93.6%
Connector Punctuation 30870
 
6.4%
Decimal Number 95
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 57759
12.8%
e 57376
12.7%
o 49486
10.9%
s 48323
10.7%
i 31889
 
7.0%
r 29702
 
6.6%
t 24536
 
5.4%
c 23062
 
5.1%
m 21106
 
4.7%
l 16364
 
3.6%
Other values (16) 93210
20.6%
Connector Punctuation
ValueCountFrequency (%)
_ 30870
100.0%
Decimal Number
ValueCountFrequency (%)
2 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 452813
93.6%
Common 30965
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 57759
12.8%
e 57376
12.7%
o 49486
10.9%
s 48323
10.7%
i 31889
 
7.0%
r 29702
 
6.6%
t 24536
 
5.4%
c 23062
 
5.1%
m 21106
 
4.7%
l 16364
 
3.6%
Other values (16) 93210
20.6%
Common
ValueCountFrequency (%)
_ 30870
99.7%
2 95
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483778
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 57759
11.9%
e 57376
11.9%
o 49486
10.2%
s 48323
10.0%
i 31889
 
6.6%
_ 30870
 
6.4%
r 29702
 
6.1%
t 24536
 
5.1%
c 23062
 
4.8%
m 21106
 
4.4%
Other values (18) 109669
22.7%

product_name_lenght
Real number (ℝ)

MISSING 

Distinct66
Distinct (%)0.2%
Missing610
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean48.476949
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-08-24T17:08:53.965457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median51
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.245741
Coefficient of variation (CV)0.21135284
Kurtosis0.19256346
Mean48.476949
Median Absolute Deviation (MAD)7
Skewness-0.90322176
Sum1567793
Variance104.9752
MonotonicityNot monotonic
2024-08-24T17:08:54.098947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 2182
 
6.6%
59 2025
 
6.1%
58 1887
 
5.7%
57 1719
 
5.2%
55 1683
 
5.1%
56 1675
 
5.1%
54 1439
 
4.4%
53 1330
 
4.0%
52 1259
 
3.8%
50 1039
 
3.2%
Other values (56) 16103
48.9%
ValueCountFrequency (%)
5 2
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 8
< 0.1%
10 5
 
< 0.1%
11 7
< 0.1%
12 13
< 0.1%
13 16
< 0.1%
14 11
< 0.1%
ValueCountFrequency (%)
76 1
 
< 0.1%
72 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
66 1
 
< 0.1%
64 59
 
0.2%
63 515
1.6%
62 65
 
0.2%
61 65
 
0.2%

product_description_lenght
Real number (ℝ)

MISSING 

Distinct2960
Distinct (%)9.2%
Missing610
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean771.49528
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-08-24T17:08:54.247797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile150
Q1339
median595
Q3972
95-th percentile2063
Maximum3992
Range3988
Interquartile range (IQR)633

Descriptive statistics

Standard deviation635.11522
Coefficient of variation (CV)0.82322632
Kurtosis4.8289228
Mean771.49528
Median Absolute Deviation (MAD)293
Skewness1.9620928
Sum24950929
Variance403371.35
MonotonicityNot monotonic
2024-08-24T17:08:54.396695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
404 94
 
0.3%
729 86
 
0.3%
651 66
 
0.2%
703 66
 
0.2%
184 65
 
0.2%
236 65
 
0.2%
303 63
 
0.2%
352 62
 
0.2%
375 60
 
0.2%
246 58
 
0.2%
Other values (2950) 31656
96.1%
(Missing) 610
 
1.9%
ValueCountFrequency (%)
4 5
< 0.1%
8 1
 
< 0.1%
15 1
 
< 0.1%
20 1
 
< 0.1%
23 1
 
< 0.1%
26 2
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
30 2
 
< 0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
3992 1
< 0.1%
3988 1
< 0.1%
3985 1
< 0.1%
3976 1
< 0.1%
3963 1
< 0.1%
3956 1
< 0.1%
3954 2
< 0.1%
3950 1
< 0.1%
3949 1
< 0.1%
3948 1
< 0.1%

product_photos_qty
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)0.1%
Missing610
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean2.1889861
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-08-24T17:08:54.532380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7367656
Coefficient of variation (CV)0.79341099
Kurtosis7.2635342
Mean2.1889861
Median Absolute Deviation (MAD)0
Skewness2.1934091
Sum70794
Variance3.0163549
MonotonicityNot monotonic
2024-08-24T17:08:54.640030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 16489
50.0%
2 6263
 
19.0%
3 3860
 
11.7%
4 2428
 
7.4%
5 1484
 
4.5%
6 968
 
2.9%
7 343
 
1.0%
8 192
 
0.6%
9 105
 
0.3%
10 95
 
0.3%
Other values (9) 114
 
0.3%
(Missing) 610
 
1.9%
ValueCountFrequency (%)
1 16489
50.0%
2 6263
 
19.0%
3 3860
 
11.7%
4 2428
 
7.4%
5 1484
 
4.5%
6 968
 
2.9%
7 343
 
1.0%
8 192
 
0.6%
9 105
 
0.3%
10 95
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 2
 
< 0.1%
17 7
 
< 0.1%
15 8
 
< 0.1%
14 5
 
< 0.1%
13 9
 
< 0.1%
12 35
 
0.1%
11 46
0.1%
10 95
0.3%

product_weight_g
Real number (ℝ)

HIGH CORRELATION 

Distinct2204
Distinct (%)6.7%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2276.4725
Minimum0
Maximum40425
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-08-24T17:08:54.784425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105
Q1300
median700
Q31900
95-th percentile10850
Maximum40425
Range40425
Interquartile range (IQR)1600

Descriptive statistics

Standard deviation4282.0387
Coefficient of variation (CV)1.8809974
Kurtosis15.133565
Mean2276.4725
Median Absolute Deviation (MAD)500
Skewness3.6048598
Sum75007492
Variance18335856
MonotonicityNot monotonic
2024-08-24T17:08:54.935444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 2084
 
6.3%
300 1561
 
4.7%
150 1259
 
3.8%
400 1206
 
3.7%
100 1188
 
3.6%
500 1112
 
3.4%
250 1001
 
3.0%
600 957
 
2.9%
350 832
 
2.5%
700 748
 
2.3%
Other values (2194) 21001
63.7%
ValueCountFrequency (%)
0 4
 
< 0.1%
2 5
 
< 0.1%
25 1
 
< 0.1%
50 312
0.9%
53 1
 
< 0.1%
54 1
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 6
 
< 0.1%
61 1
 
< 0.1%
ValueCountFrequency (%)
40425 1
 
< 0.1%
30000 143
0.4%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 2
 
< 0.1%
29600 5
 
< 0.1%
29500 2
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.815078
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-08-24T17:08:55.086898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile65
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.914458
Coefficient of variation (CV)0.54890201
Kurtosis3.513618
Mean30.815078
Median Absolute Deviation (MAD)8
Skewness1.7504597
Sum1015326
Variance286.09889
MonotonicityNot monotonic
2024-08-24T17:08:55.227023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 5520
 
16.8%
20 2816
 
8.5%
30 2029
 
6.2%
18 1502
 
4.6%
25 1387
 
4.2%
17 1310
 
4.0%
19 1270
 
3.9%
40 1224
 
3.7%
22 972
 
2.9%
35 968
 
2.9%
Other values (89) 13951
42.3%
ValueCountFrequency (%)
7 1
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
10 3
 
< 0.1%
11 16
 
< 0.1%
12 15
 
< 0.1%
13 20
 
0.1%
14 40
 
0.1%
15 48
 
0.1%
16 5520
16.8%
ValueCountFrequency (%)
105 149
0.5%
104 19
 
0.1%
103 9
 
< 0.1%
102 19
 
0.1%
101 18
 
0.1%
100 119
0.4%
99 16
 
< 0.1%
98 16
 
< 0.1%
97 7
 
< 0.1%
96 4
 
< 0.1%

product_height_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.937661
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-08-24T17:08:55.371804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q321
95-th percentile44
Maximum105
Range103
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.637554
Coefficient of variation (CV)0.80516158
Kurtosis6.6786189
Mean16.937661
Median Absolute Deviation (MAD)6
Skewness2.1400613
Sum558079
Variance185.98288
MonotonicityNot monotonic
2024-08-24T17:08:55.520736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2548
 
7.7%
15 2022
 
6.1%
20 1991
 
6.0%
16 1595
 
4.8%
11 1551
 
4.7%
5 1529
 
4.6%
12 1522
 
4.6%
8 1467
 
4.5%
2 1357
 
4.1%
7 1235
 
3.7%
Other values (92) 16132
49.0%
ValueCountFrequency (%)
2 1357
4.1%
3 888
 
2.7%
4 1176
3.6%
5 1529
4.6%
6 1138
3.5%
7 1235
3.7%
8 1467
4.5%
9 1068
3.2%
10 2548
7.7%
11 1551
4.7%
ValueCountFrequency (%)
105 24
0.1%
104 5
 
< 0.1%
103 4
 
< 0.1%
102 8
 
< 0.1%
100 15
< 0.1%
99 1
 
< 0.1%
98 2
 
< 0.1%
97 2
 
< 0.1%
96 4
 
< 0.1%
95 7
 
< 0.1%

product_width_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.196728
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.6 KiB
2024-08-24T17:08:55.668670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile47
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.079047
Coefficient of variation (CV)0.52072203
Kurtosis4.0731259
Mean23.196728
Median Absolute Deviation (MAD)6
Skewness1.6709713
Sum764309
Variance145.90339
MonotonicityNot monotonic
2024-08-24T17:08:55.806330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 3718
 
11.3%
20 3053
 
9.3%
16 2808
 
8.5%
15 2393
 
7.3%
30 1786
 
5.4%
12 1536
 
4.7%
25 1329
 
4.0%
14 1264
 
3.8%
13 1133
 
3.4%
17 1118
 
3.4%
Other values (85) 12811
38.9%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 4
 
< 0.1%
8 9
 
< 0.1%
9 15
 
< 0.1%
10 23
 
0.1%
11 3718
11.3%
12 1536
4.7%
13 1133
 
3.4%
14 1264
 
3.8%
15 2393
7.3%
ValueCountFrequency (%)
118 1
 
< 0.1%
105 5
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 13
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 1
 
< 0.1%

Interactions

2024-08-24T17:08:50.886531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:45.308785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:46.339171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:47.275152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:48.210339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:49.162919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:50.017472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:51.011358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:45.435878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:46.471389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:47.403628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:48.347717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:49.283256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:50.139583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:51.143834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:45.702601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:46.611136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:47.558281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:48.498626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:49.416091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:50.272113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:51.274974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:45.836243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:46.751173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:47.698899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:48.641022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:49.544724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:50.403918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:51.410309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:45.974160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:46.895017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:47.837137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:48.779742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:49.677649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:50.537849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:51.523746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:46.091813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:47.019997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:47.959573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:48.903500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:49.786471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:50.651028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:51.640638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:46.214000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:47.146485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:48.084102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:49.029857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:49.901637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-08-24T17:08:50.767832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-08-24T17:08:55.923416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
product_description_lenghtproduct_height_cmproduct_length_cmproduct_name_lenghtproduct_photos_qtyproduct_weight_gproduct_width_cm
product_description_lenght1.0000.100-0.0080.1000.1250.108-0.056
product_height_cm0.1001.0000.249-0.039-0.0390.5230.364
product_length_cm-0.0080.2491.0000.0800.0400.6170.613
product_name_lenght0.100-0.0390.0801.0000.1530.0990.068
product_photos_qty0.125-0.0390.0400.1531.0000.0100.002
product_weight_g0.1080.5230.6170.0990.0101.0000.605
product_width_cm-0.0560.3640.6130.0680.0020.6051.000

Missing values

2024-08-24T17:08:51.807581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-24T17:08:52.151708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-24T17:08:52.391610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

product_idproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cm
01e9e8ef04dbcff4541ed26657ea517e5perfumaria40.0287.01.0225.016.010.014.0
13aa071139cb16b67ca9e5dea641aaa2fartes44.0276.01.01000.030.018.020.0
296bd76ec8810374ed1b65e291975717fesporte_lazer46.0250.01.0154.018.09.015.0
3cef67bcfe19066a932b7673e239eb23dbebes27.0261.01.0371.026.04.026.0
49dc1a7de274444849c219cff195d0b71utilidades_domesticas37.0402.04.0625.020.017.013.0
541d3672d4792049fa1779bb35283ed13instrumentos_musicais60.0745.01.0200.038.05.011.0
6732bd381ad09e530fe0a5f457d81becbcool_stuff56.01272.04.018350.070.024.044.0
72548af3e6e77a690cf3eb6368e9ab61emoveis_decoracao56.0184.02.0900.040.08.040.0
837cc742be07708b53a98702e77a21a02eletrodomesticos57.0163.01.0400.027.013.017.0
98c92109888e8cdf9d66dc7e463025574brinquedos36.01156.01.0600.017.010.012.0
product_idproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cm
329416ec96c91757fad0aecafc0ee7f262dccbebes62.01417.01.09550.036.035.035.0
3294216280ca280a86fee2ba3c928ed04439fmoveis_decoracao64.0236.011.02200.031.011.026.0
329433becff10d1deb92b02f2a1ee62a04524informatica_acessorios54.01520.02.06150.030.030.020.0
329441a14237ecc2fe3772b55c8d4e11ccb35moveis_decoracao58.01405.03.0150.035.02.025.0
32945c4e71b64511b959455e2107fe7859020utilidades_domesticas59.01371.02.0200.018.015.015.0
32946a0b7d5a992ccda646f2d34e418fff5a0moveis_decoracao45.067.02.012300.040.040.040.0
32947bf4538d88321d0fd4412a93c974510e6construcao_ferramentas_iluminacao41.0971.01.01700.016.019.016.0
329489a7c6041fa9592d9d9ef6cfe62a71f8ccama_mesa_banho50.0799.01.01400.027.07.027.0
3294983808703fc0706a22e264b9d75f04a2einformatica_acessorios60.0156.02.0700.031.013.020.0
32950106392145fca363410d287a815be6de4cama_mesa_banho58.0309.01.02083.012.02.07.0